high-frequency trading market-making optimization example#153
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mnabi-paxai wants to merge 2 commits into
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high-frequency trading market-making optimization example#153mnabi-paxai wants to merge 2 commits into
mnabi-paxai wants to merge 2 commits into
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This example demonstrates why GPU acceleration is required for realistic high-frequency trading market-making, proving that CPU cannot meet real-time constraints while GPU enables practical deployment. Key features: - Realistic non-linear optimization with 50+ trading pairs - CPU baseline: ~200-300ms solve time (5-6x over budget) - GPU expected: ~7ms solve time (35x speedup) - Complete Jupyter notebook with benchmarks and visualizations - Sample data generation (no API keys required) - Educational content for financial engineers and students Problem formulation includes: - Exponential fill probability: P(fill) = exp(-k * spread) - Transaction costs and slippage - Inventory risk with volatility weighting - 150 decision variables (50 pairs × 3 variables each) This addresses the "CPU cannot do this in real-time, GPU makes it possible" use case for cuOpt, demonstrating GPU necessity rather than just speedup. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com> Signed-off-by: MarziehNabi <mnabi@paxai.co>
This comprehensive HTML page provides a visually appealing explanation of: - The market-making optimization problem - Mathematical formulation with non-linear terms - GPU necessity justification (not just faster, but required) - CPU vs GPU benchmark comparisons with charts - 30-minute simulation results proving CPU fails budget - Interactive performance visualizations - Educational content for presentations and portfolios Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com> Signed-off-by: MarziehNabi <mnabi@paxai.co>
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Summary
This PR adds a comprehensive high-frequency trading (HFT) market-making optimization example that demonstrates why GPU acceleration is required for realistic
This PR adds a comprehensive high-frequency trading (HFT) market-making optimization example that demonstrates why GPU acceleration is required for realistic financial optimization, not just "faster" but essential for practical deployment.
Key Results
CPU Result: 5x over budget, cannot maintain 10Hz quote updates → unprofitable
GPU Result: Comfortable real-time operation → enables profitable strategy
This is not about "faster" - it's about "possible vs impossible."
What's Included
Problem: Non-linear optimization with 50+ trading pairs
30-Minute Simulation Results
Benchmarks
Testing
References